It is well known that collaborative papers tend to receive more citations than solo-authored papers. Here we try to identify the subtle factors of this collaborative effect by analyzing metadata and citation counts for co-authored papers in the biomedical domain, after accounting for attributes known to be strong predictors of citation count. Article-level metadata were gathered from 98,000 PubMed article records categorized with the term breast neoplasm, a topic offering longevity and relevance across biomedical subdisciplines, and yielding a relatively large sample size. Open access citation data was obtained from PubMed Central (PMC). Author-level attributes were encoded from disambiguated author name data in PubMed and appended as article-level attributes of collaborations. A logistic regression model was built to assess the relative weights of these factors as influences on citation counts. As expected, the journal and language of the paper were the strongest predictors. The significance of the number of authors diminished after accounting for other attributes. Some of the more subtle predictors included the group's highest hindex, which was positively correlated, while the diversity of author h-indices, minimum professional age, and author's total unique collaborators were negatively correlated. These observations indicate that smaller collaborations composed of early superstars -young, rapidly successful researchers with relatively high and similar h-indices -may be at least as influential in biomedical research as larger collaborations with different demographics. While minimum h-index was important, the first author's h-index was insignificant, underscoring the importance of the middle authors' publishing history. The gender diversity outcomes suggest that mixed groups may be ideal, and further research in this area is indicated.

This study identifies challenges and promising directions in the curation of 3D data. 3D visualization shows great promise for a range of scholarly fields through interactive engagement with and analysis of spatially complex artifacts, spaces, and data. While the new affordability of emerging 3D capture technologies presents greater academic possibilities, academic libraries need more effective workflows, policies, standards, and practices to ensure that they can support the creation, discovery, access, preservation, and reproducibility of 3D data sets. This study uses nominal group technique with invited experts across several disciplines and sectors to identify common challenges in the creation and re-use of 3D data for the purpose of developing library strategy for supporting curation of 3D data. This article identifies staffing needs for 3D imaging; alignment with IT resources; the roll of archivists in addressing unique challenges posed by these datasets; the importance of data annotation, metadata, and transparency for research integrity and reproducibility; and features for storage, access, and management to facilitate re-use by researchers and educators. Participants identified three main challenges for supporting 3D data that align with the strengths of libraries: 1) development of crosswalks and aggregation tools for discipline-specific metadata models, data dictionaries for 3D research, and aggregation tools for expanding discovery; 2) development of an open source viewer that supports streaming and annotation on archival formats of 3D models and makes archival master files accessible, while also serving derivative files based on user requirements; and 3) widespread of adoption of better documentation and technical metadata for image capture and modeling processes in order to support replicability of research, reproducibility of models, and transparency of scientific process.

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Objective: The purpose of this article is to explore data visualization as a consulting service offered by a research library with particular attention to uses of visualization at various places within the research lifecycle. Methods:Lessons learned from a year of offering data visualization as a consulting service, and two general case studies are offered.Results: Data visualization consulting services have a few unique considerations, including setting clear expectations, considering proprietary vs open source technologies, and making sure the consulting experience is also a learning experience. In addition, we can clearly place data visualization requests, in the form of profiled case studies, in multiple parts of the research lifecycle.